ABSTRACT Recent findings have implicated the gut microbiota as a contributor of metabolic diseases through the modulation of host metabolism and inflammation. Atherosclerosis is associated

with lipid accumulation and inflammation in the arterial wall, and bacteria have been suggested as a causative agent of this disease. Here we use shotgun sequencing of the gut metagenome to

demonstrate that the genus _Collinsella_ was enriched in patients with symptomatic atherosclerosis, defined as stenotic atherosclerotic plaques in the carotid artery leading to

cerebrovascular events, whereas _Roseburia_ and _Eubacterium_ were enriched in healthy controls. Further characterization of the functional capacity of the metagenomes revealed that patient

CONTENT BEING VIEWED BY OTHERS MICROBIOME CONNECTIONS WITH HOST METABOLISM AND HABITUAL DIET FROM 1,098 DEEPLY PHENOTYPED INDIVIDUALS Article 11 January 2021 FROM OBESITY THROUGH GUT

MICROBIOTA TO CARDIOVASCULAR DISEASES: A DANGEROUS JOURNEY Article 20 July 2020 NEIL3-DEFICIENCY INCREASES GUT PERMEABILITY AND CONTRIBUTES TO A PRO-ATHEROGENIC METABOLIC PHENOTYPE Article

Open access 05 October 2021 INTRODUCTION The gut metagenome has been implicated as an environmental factor influencing adiposity and obesity by modulating host lipid metabolism1,2,3,4. The

gut microbiota is also a source of inflammatory molecules such as lipopolysaccharide and peptidoglycan that may contribute to metabolic disease2,5,6. Whole-genome metagenomic sequencing has

provided knowledge about the structure of the human gut microbiome and identified a large number of genes and direct links to functional information7,8. Links between the gut metagenome and

human diseases have been investigated, showing that obesity is associated with alterations in the gut metagenome and reduced bacterial diversity9. Going beyond traditional comparative

analysis of functional components, the integration of metagenomic data with metabolic network analysis provides deeper understanding of metabolic capabilities of the metagenome10, and this

approach could be very useful for mechanistically delineating the link between the gut metagenome and human health. Atherosclerotic disease, with manifestations such as myocardial infarction

and stroke, is characterized by accumulation of cholesterol and recruitment of macrophages to the arterial wall. The gut microbiota has been shown to metabolize the dietary lipid

phosphatidylcholine to trimethyl amine, which promotes atherosclerosis and inflammation in mice, furthermore levels of choline, trimethylamine N-oxide and betaine have been found to predict

cardiovascular disease (CVD) risk in humans11. In a recent study, we pyrosequenced the 16_S_ rRNA gene and showed that atherosclerotic plaques contain bacterial DNA with phylotypes common to

the gut microbiota and that the amount of bacterial DNA in the plaque correlated with inflammation12. However, it is unclear whether atherosclerosis is associated with alterations in the

composition of the gut metagenome. To address this issue, we sequenced the gut metagenomes of patients with symptomatic atherosclerotic plaques and gender- and age-matched controls without

large vulnerable plaques in the carotid arteries. To analyse the data, we developed and used a bioinformatics pipeline, Metagenomic Data Utilization and Analysis, (MEDUSA) that, besides

identification of species abundance, also allows for _de novo_ assembly and the identification of enriched metabolic functions in the metagenome. Our data show that patients were enriched in

the genus _Collinsella_ whereas controls were enriched in _Eubacterium_ and _Roseburia._ At the functional level, patient metagenomes were enriched in genes encoding peptidoglycan

biosynthesis whereas those of healthy controls were enriched in phytoene dehydrogenase genes. RESULTS TAXONOMIC CHARACTERIZATION OF THE GUT MICROBIOTA To address whether the gut metagenome

is associated with symptomatic atherosclerosis, we sequenced the fecal metagenome of 12 patients with symptomatic atherosclerotic plaques (who had undergone carotid endarterectomy for minor

ischemic stroke, transient ischemic attack or amaurosis fugax) and 13 gender- and age-matched controls without large vulnerable plaques in the carotid arteries (Table 1). In total, we

generated 337 million 100 bp paired-end reads (12.5±4.7 (s.d.) million reads per sample) that, first, were trimmed and filtered to only contain non-human reads longer than 35 bp (Fig. 1a).

To determine the composition of the gut microbiota, we aligned the reads to a catalog of 2,382 non-redundant reference genomes (Supplementary Data 1) collected from National Center for

Biological Information (NCBI) and Human Microbiome Project catalog (http://hmpdacc.org). On average, 28% of the reads in a sample could be aligned to any reference genome, which is close to

the 31% found in a previous metagenomic study using Illumina reads7. The majority (98±4% (s.d.) of aligned reads were bacterial and dominated by the phyla Firmicutes and Bacteroides,

representing 56% and 29% of the microbiota, respectively, followed by Actinobacteria (6%) and Proteobacteria (4%; Supplementary Fig. S1). This distribution is in agreement with previous

observations13,14. The archael phylum Euryarchaeota was also present but with a high inter-subject variation (2.0±4.3% (s.d.); Supplementary Fig. S1) and was dominated by the species

_Methanobrevibacter smithii_, which constituted at least 93% of the reads assigned to Euryarchaeota in any individual. _Bacteroides, Ruminococcus, Eubacterium_ and _Faecalibacterium_ were

the most abundant genera in our cohort (Supplementary Fig. S2) as found previously7,13. Species and genome level abundances were also calculated (Supplementary Figs S3 and S4), and

_Faecalibacterium prausnitzii_ was shown to be the most abundant species. At coverage of at least 1% of aligned reads to reference genomes, we identified 82 species in all 27 subjects making

up the core microbiota in our cohort (Supplementary Data 2). By contrast, the MetaHIT study identified 18 species in their total cohort of 124 individuals and 75 in half of the individuals

at 1% coverage7. This difference may be explained by the fact that our cohort was smaller and more homogenous (that is, individuals of a similar age living in the same area) than the MetaHIT

cohort, which included healthy subjects as well as patients with obesity or inflammatory bowel disease from different countries. PCA AND ENTEROTYPES IN THE COHORT An instrumental principal

component analysis with the health status as instrumental variable revealed that the microbial species abundance separated patients and healthy controls (Fig. 1b, _P_=1e−4, Monte Carlo

simulation). The genus _Collinsella_ was enriched in patients whereas _Eubacterium_ and _Roseburia_ and three species of _Bacteroides_ were enriched in control subjects ((adjusted) adj.

_P_<0.05, Wilcoxon rank-sum test; Fig. 1c). Several bacterial groups correlated with cardiovascular risk factors (Fig. 1d); in particular, genera of _Clostridiales, Clostridium_ sp. SS2/1

and the poorly characterized butyrate-producing bacterium SSC/2 negatively correlated with the inflammatory marker high-sensitivity C-reactive protein (hsCRP; Fig. 1d and Supplementary Fig.

S5). A recent study suggests that the human gut microbiota can be stratified into three enterotypes of distinct microbial compositions13. We analysed our samples according to this earlier

study13, calculated the Jensen–Shannon distance of the genus abundance and clustered samples with partitioning around mediods. The Calinski–Harabasz index indicated that the optimal number

of clusters was three (Fig. 2a and Supplementary Fig. S6). However, when the average silhouette index was used to assess the quality of the clusters, we saw the highest silhouette index with

two clusters (Supplementary Figs S6–7), which has also been observed previously15. We chose, however, to use three clusters as proposed in the publication by Arumugam _et al_.13, which is

the largest enterotypes study to date. The three enterotypes that we observed were characterized by the same contributors at the genus level as shown previously13: _Bacteroides_ contributed

to enterotype 1, _Prevotella_ contributed to entrotype 2 and _Ruminococcus_ contributed to enterotype 3 (Fig. 2b and Supplementary Fig. S8). However, as described previously13, the third

enterotype may be identified by different contributors depending on the source of sequence data and we found this cluster to be characterized by low levels of _Bacteroides_ and _Prevotella_

rather than a dominant genus (Supplementary Fig. S8). To test whether the enterotypes were associated with disease status, we used Fisher’s exact test and showed that patients were

underrepresented in enterotype 1 (_P_=0.0048, Fisher’s exact test) and overrepresented in enterotype 3 (_P_=0.047, Fisher’s exact test; Supplementary Table S1). METABOLIC FUNCTIONS OF THE

GUT MICROBIOTA To discover new genes in the metagenome, we performed _de novo_ assembly of the sequence data, first for each individual sample separately and subsequently for a pool of all

the non-assembled data from the individual samples to create one global gene catalog of our cohort. A total of 1.7 Gbp of contigs longer than 500 bp could be assembled and with a N50 value

of 1.8 kbp using 3 as coverage cutoff and kmer of 31. MetaGeneMark16 was used to predict genes from the contig set and 2.6 million open reading frames representing 1.4 million non-redundant

genes were found. The genes were functionally annotated to KEGG, Pfam and carbohydrate active enzyme (CAZy) databases and their relative abundance was assessed. On average, 60% of the reads

could be aligned to the set of contigs, which is substantially more than the percentage of reads (28%) that could be aligned to the reference genomes. This indicates that our gene catalog

contains a majority of the sequenced microbiome. A global analysis of the abundance of KEGG orthologies (KO) resulted in separation of the patient group from the control group (Supplementary

Fig. S9). In total, 225 KOs were differentially abundant (adj. _P_<0.05, Wilcoxon rank-sum test), illustrating that there were functional aspects of the gut metagenome associated with

symptomatic atherosclerosis. Enriched metabolic functions in the metagenomes of patients and controls can be assessed by integrating the relative gene abundance with metabolic networks. We

used the reporter feature algorithm17,18, and based on the KEGG metabolic network and the pathway associations for the KOs together with the corrected _P_-values, we identified first,

reporter pathways (for example, pathways containing several significantly differentially abundant KOs; Supplementary Table S2) and second reporter metabolites (for example, metabolites

around which there are enzymatic reactions with associated KO differentially abundant; Supplementary Table S3). The peptidoglycan biosynthesis pathway was the highest scoring reporter

pathway; eight peptidoglycan biosynthetic KOs were enriched in the gut metagenomes of patients and one was enriched in controls (adj. _P_<0.05, Wilcoxon rank-sum test, Fig. 3a).

Consequently, we also found several of the metabolites in the peptidoglycan pathway to be reporter metabolites, for example, UDP-_N_-acetyl-D-glucosamine, which is a key precursor for

peptidoglycan, indicating significant changes in KOs linked to these metabolites. There were also features of the metagenome that correlated negatively with inflammation, the highest scoring

association being butyrate-acetoacetate CoA-transferase (K01036) with hsCRP (Spearman’s _ρ_=−0.73, adj. _P_=0.04). These findings are in agreement with a previous study showing that

butyrate is an important negative regulator of inflammation19. To investigate the origin of the butyrate-acetoacetate CoA-transferase genes, we performed a BLASTP search and identified the

source as _Clostridium_ sp. SS2/1; as discussed above, this species also negatively correlated with hsCRP (Fig. 1d and Supplementary Fig. S5). A recent metabolomics study showed that three

microbially modulated metabolites of dietary phosphatidylcholine metabolism (choline, trimethylamine N-oxide and betaine) correlate with CVD in humans11. We reconstructed the metabolic

pathway from phosphatidylcholine to trimethylamine (Supplementary Fig. S10) but did not observe any significant association of gene abundance in this pathway with atherosclerosis. However,

we observed a positive correlation between plasma triglycerides and the abundance of several KOs in the pathway for fatty acid metabolism, specifically β-oxidation (Fig. 3b, Supplementary

Fig. S11), which suggests a strong interaction between the gut microbiota and dietary components. We also observed that the GS-GOGAT system, which the microbiota uses for assimilation of

nitrogen into amino acids, was significantly enriched in the patient group (Fig. 3c). In particular, the ATP-dependent reaction carried out by glutamine synthase (adj. _P_=0.035, Wilcoxon

rank-sum test) and the glutamate synthase large and small subunits (adj. _P_=0.013 and adj. _P_=0.0074, Wilcoxon rank-sum test, respectively) were enriched in patient microbiota. The

ATP-independent glutamate dehydrogenase was not found to be different between the groups. Interestingly, phytoene dehydrogenase (K10027), which is involved in the metabolism of lipid-soluble

antioxidants (such as the carotenoids lycopene and β-carotene), was the KO most significantly enriched in controls in our study (adj. _P_=0.0046, Wilcoxon rank-sum test, Fig. 4). To

determine the phylogenetic origin of the 13 genes annotated as phytoene dehydrogenases in this study, we used BLASTP to search for related sequences in the NCBI nr database. Seven of the

genes matched to _Bacteroides_, two to _Clostridia,_ two to _Prevotella_ and the remaining two to Actinobacteria and various Bacteroidetes. We evaluated whether the enrichment of phytoene

dehydrogenase was accompanied by increased levels of carotenoids, and found increased levels of β-carotene (_P_=0.05, Student’s _t_-test), but not lycopene, in serum of healthy controls

compared with patients (Fig. 4, Supplementary Figs S12–13). DISCUSSION In this study, we identified several compositional and functional alterations of the gut metagenome that may be related

to symptomatic atherosclerosis. The differences in the metagenome between patients and controls did not seem to be related to smoking, diabetes or body mass index (Supplementary Figs

S14–S19), but these factors and the potentially modifying effects of different types of medication and diet that may be different between patients and controls require further investigation.

Interestingly, we observed enrichment of patients within the _Ruminococcus_ enterotype. The metagenomes of patients were enriched in genes associated with peptidoglycan biosynthesis, which

suggests that increased peptidoglycan production by the gut metagenome may contribute to symptomatic atherosclerosis by priming the innate immune system and enhancing neutrophil function.

Indeed, inflammation has been identified as an important contributor to the pathogenesis of atherosclerosis20. The increased abundance of genes in this pathway cannot be explained solely by

a general increase in Gram-positive bacteria because both Gram-positive and Gram-negative bacteria have peptidoglycan and even more, abundant Gram-positive groups of bacteria such as

_Eubacterium_ and _Roseburia_ were enriched in controls. Our finding of enriched levels of phytoene dehydrogenase in the metagenomes of healthy controls and its association with elevated

levels of β-carotene in the serum may indicate that the possible production of this anti-oxidant by the gut microbiota may have a positive health benefit. Lycopene and β-carotene adipose

levels are associated with a reduced risk of CVD in epidemiological studies21,22, but several large randomized, placebo-controlled studies with durations up to 12 years have failed to show

that supplementation of pure β-carotene reduces CVD risk23,24. However, lycopene has been related to intima-media thickness of the common carotid artery25 and suggested to have a role in the

early stage and prevention of atherosclerosis26. A previous study encompassing >500 participants failed to observe an association between lycopene intake and plasma lycopene levels27,

indicating that other mechanisms might be more important in determining plasma levels than oral intake of lycopene. Together with evidence that bacterial species from the human gut can

synthesize carotenoids28,29, we propose that our findings of increased prevalence of phytoene dehydrogenase, and increased levels of β-carotene in plasma of control subjects represent an

important step towards elucidating the importance of carotenoids in the development of atherosclerosis. It is worth noting that peptidoglycan and phytoene dehydrogenase genes were not linked

to obesity as there was no significant difference in abundance of these genes between lean and overweight/obese subjects in our study (Supplementary Fig. S14), or in the meta-analysis of an

independent study13 (Supplementary Fig. S17). In conclusion, here we observed associations between enterotypes, genera and species and symptomatic atherosclerosis at the taxonomical level.

Within the metagenome, genes in the peptidoglycan pathway were enriched in patients, whereas genes involved in synthesis of anti-inflammatory molecules (for example, butyrate) and

antioxidants were enriched in controls, suggesting that the metagenome may contribute to the development of symptomatic atherosclerosis by acting as a regulator of host inflammatory

pathways. Even though our study cannot provide evidence for direct causal effects, these findings indicate that the gut metagenome may have a role in the development of symptomatic

atherosclerosis. METHODS STUDY DESIGN AND RECRUITMENT The patient samples were from the Göteborg Atheroma Study Group Biobank, which includes samples from patients who had undergone surgery

to excise an atherosclerotic plaque30. The study was approved by the Ethics Committee in Gothenburg. All subjects gave written informed consent to participate after receiving oral and

written information. All patients had severely stenotic plaques in the carotid artery with ipsilateral manifestations of emboli to either the brain, as minor brain infarction or transient

ischemic symptoms, or to the retinal artery (Table 1). The clinical definition of minor brain infarction corresponds to a patient who has mild and no severe functional deficits without any

need of prolonged hospital care. Hence, the underlying etiology in all these patients was a vulnerable atherosclerotic plaque with plaque rupture and embolism leading to operations with

excision of the plaque30. It is not likely that the clinical events _per se_ directly influenced the gut metagenome, as minor stroke has no acute effects on CRP and white blood cell count31

and because the patients only had transient or minor tissue-damaging effects in the brain or eye. The control group was selected to represent an age- and sex-matched group with no

cardiovascular health problems and was recruited from two on-going population-based cohorts that have been described previously32,33. The investigations of the control group included

repeated ultrasound examinations of the carotid and femoral arteries, and no large, potentially vulnerable plaques were detected. Further inclusion criteria in the control group were no

history of CVD, no smoking, no diabetes and no treated hyperlipidemia. The underlying rationale was to avoid subjects with vulnerable plaques defined as echo-thin plaques with stenosis

>50% of vessel lumen34,35. Analysis of updated health records showed that one control subject had a dilation of ascending aorta as the initial recruitment as ‘healthy control’ and a

second had white matter disease in the brain, possibly due to a small artery disease. As these diagnoses may have atherosclerosis as underlying cause, we excluded these subjects from

analyses of differences between patients and controls, although they were included in specified analyses of the total cohort. Blood samples were drawn before surgery and plasma and serum

samples were prepared and immediately frozen at −70 °C. The subjects were given material and instructions for providing fecal samples at home. Methods for processing fecal samples and

isolation of metagenomic DNA have been described previously36. SEQUENCING All samples were sequenced in the Illumina HiSeq2000 instrument at SciLifeLab in Stockholm, Sweden, with up to ten

samples pooled in one lane. Libraries were prepared with a fragment length of ~300 bp. Paired-end reads were generated with 100 bp in the forward and reverse direction. DATA QUALITY CONTROL

Sequencing adapter sequences were removed with cutadapt (http://code.google.com/p/cutadapt/). The length of each read was trimmed with SolexaQA with the options ‘-b –p 0.05’37. Read pairs

with either reads shorter than 35 bp were removed with a custom Python script. The high-quality reads were then aligned to the human genome (NCBI version 37) with Bowtie38 using ‘-n 2 -l 35

-e 200 –best -p 8 –chunkmbs 1024 -X 600 –tryhard’. This set of high-quality reads were then used for further analysis. ALIGNMENT TO REFERENCE GENOMES AND TAXONOMICAL ANALYSIS A set of 2,382

microbial reference genomes were obtained from the NCBI and Human Microbiome Project on 02 August 2011. The reference genomes were combined into two Bowtie indexes and the metagenomic

sequence reads were aligned to the reference genomes using Bowtie with parameters ‘-n 2 -l 35 -e 200 –best -p 8 –chunkmbs 1024 -X 600 –tryhard’. Mapping results were merged by selecting the

alignment with fewest mismatches; if a read was aligned to a reference genome with the same number of mismatches, each genome was assigned half to each genome. The relative abundance of each

genome was calculated by summing the number of reads aligned to that genome divided by the genome size. In each subject, the relative abundance was scaled to sum to one. The taxonomic rank

for every genome was downloaded from NCBI taxonomy to assign each genome to a species, genus and phyla. The relative abundance for each taxonomical rank was calculated buy summing the

relative abundance of all its members. _DE NOVO_ ASSEMBLY AND GENE CALLING The high-quality reads were used for _de novo_ assembly with Velvet39 into contigs of at least 500-bp length using

3 as coverage cutoff and kmer length of 31. To obtain long contigs with high specificity, we iteratively explored parameter values for the kmer length and coverage cutoff to balance the

total assembly length and the N50 value to be used in the final _de novo_ assembly. Reads from each subject were used in separate assemblies and unassembled reads were then used in a global

final assembly. Genes were predicted on the contigs with MetaGeneMark16. All genes were then aligned on the contigset with Bowtie using the same parameters as above. The abundance of a gene

was calculated by counting the number of reads that align to the gene normalizing by the gene length and the total number of reads aligned to any contig. GENE ANNOTATION The genes were

annotated to the KEGG database with hidden Markov models (HMMs). Protein sequences for microbial orthologs were downloaded and aligned with MUSCLE40. HMMs were generated with HMMer3 (ref.

41) for each KO. Each gene was queried on the 4,283 HMMs and annotated the KO with lowest scoring _E_-value below 10−20. Out of the 2,645,414 genes, 848,353 (32%) were annotated to KOs. The

genes were also annotated to CAZy42. The CAZy proteins of bacterial and archaeal origin were downloaded and HMMs were built and genes annotated as described above. The feature abundance (KOs

and CAZy) was calculated by summing the abundance of genes annotated to a feature. Genes for betaine reductase were collected from two species, _Clostridium difficile_ 630 (Entrez protein

accession codes Gi: 126699967 and GI: 126699969) and _Carboxydothermus hydrogenoformans_ Z-2901 (Entrez protein accession codes Gi: 78044558 and GI: 78044225). The gene catalogue was

searched against these four genes with USEARCH43 using an _E_-value cutoff of 10−30. STATISTICAL ANALYSIS To determine differential abundance of metagenomic features (that is, taxonomic and

functional features between patients and controls) Wilcoxon rank-sum test was applied. Strains and genera with a relative abundance in any subject above 10−5 and 10−3, respectively, were

included in the analysis. Correlations were done between serum biomarkers and metagenomic features with Spearman’s correlation. _P_-values were adjusted with false discovery rate with the

method from Benjamini and Hochberg44 when multiple hypotheses were considered simultaneously and are denoted adj. P. The R package ade4 using instrumental principal component analysis45 was

used to determine the global analysis of species abundance between patients and controls (in Fig. 1b and Supplementary Figs S7,S9,S18,S19). Monte Carlo test on the between-groups inertia

percentage was performed 10,000 permutations to calculate a _P_-value in Fig. 1b. TESTING THE ASSOCIATION BETWEEN MICROBIAL GENES AND OBESITY We analysed data from Arumugam _et al_.13 to

investigate whether the abundance of peptidoglycan and phytoene dehydrogenase genes in the gut metagenome differed between obese and lean subjects. The corresponding clusters of orthologous

groups was identified to the KOs involved in peptidoglycan biosynthesis and phytoene dehydrogenase. The results are presented in Supplementary Figs S14–S17. There was no significant

differential abundance of the studied corresponding clusters of orthologous groups between healthy lean and obese subjects (Wilcoxon rank-sum test). MEASUREMENT OF Β-CAROTENE AND LYCOPENE

β-Carotene and lycopene were measured in the serum from healthy controls and patients using a modified protocol from46. Briefly, 200 μl of serum was mixed with 200 μl of ethanol and 8 μl of

0.191 mmol l−1 retinyl propionate in ethanol. Samples were vortexed gently and then 1 ml hexane was added; the samples were again vortexed (for 30 s). The phases were separated by

centrifugation at 1,500_g_ for 5 min and 900 μl of the upper phase was then transferred to a new tube. The samples were dried under low pressure at room temperature in a Speedvac

concentrator, not to complete dryness. The residue was dissolved in 100 μl ethanol followed by addition of 100 μl acetonitrile. Samples were protected from light during handling and

preparation. The compounds were measured using a Dionex HPLC system with a C18 column, maintained at 29 °C. The mobile phase was ethanol and acetonitrile (1:1) with 0.1 ml l−1 diethylamine

and was kept at a flow rate of 0.9 ml min−1. Samples were stored at 4 °C before injection of 50 μl. Chromatograms for absorbance at the wavelengths 300, 325 and 450 nm were collected

simultaneously for 20 min. Peaks were identified by comparing retention time with a standard solution of β-carotene and lycopene. Quantification was based on the area under the curve.

ADDITIONAL INFORMATION HOW TO CITE THIS ARTICLE: Karlsson F. H. _et al_. Symptomatic atherosclerosis is associated with an altered gut metagenome. _Nat. Commun._ 3:1245 doi:

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Chem._ 40, 411–416 (1994). CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS We acknowledge Rosie Perkins for critically reading and editing the manuscript, Suwanee Jansa-Ard

for technical assistance with HPLC measurements, Swedish National Infrastructure for large-scale sequencing for performing the Illumina sequencing and our colleagues of the Göteborg

Atheroma Study Group (Sahlgrenska University Hospital and University of Gothenburg) and Marie Louise Ekholm for providing clinical specimens. The bioinformatic computations were performed on

resources provided by the Swedish National Infrastructure for Computing (SNIC) at C3SE. This study was funded by Knut and Alice Wallenberg Foundation, the Chalmers Foundation, Swedish Heart

Lung Foundation, Torsten Söderberg’s Foundation, IngaBritt och Arne Lundbergs foundation, AFA Insurances, the Swedish Research Council and the Swedish Foundation for Strategic Research.

AUTHOR INFORMATION Author notes * Frida Fåk and Intawat Nookaew: These authors contributed equally to this work AUTHORS AND AFFILIATIONS * Department of Chemical and Biological Engineering,

Chalmers University of Technology, Gothenburg, SE-412 96, Sweden Fredrik H. Karlsson, Intawat Nookaew, Dina Petranovic & Jens Nielsen * Department of Molecular and Clinical Medicine,

Wallenberg Laboratory for Cardiovascular and Metabolic Research, Institute of Medicine, University of Gothenburg, Gothenburg, SE-413 45, Sweden Frida Fåk, Valentina Tremaroli, Björn

Fagerberg & Fredrik Bäckhed * Center for Cardiovascular and Metabolic Research, Gothenburg, SE-413 45, Sweden Frida Fåk, Intawat Nookaew, Valentina Tremaroli, Björn Fagerberg & 

Fredrik Bäckhed Authors * Fredrik H. Karlsson View author publications You can also search for this author inPubMed Google Scholar * Frida Fåk View author publications You can also search

for this author inPubMed Google Scholar * Intawat Nookaew View author publications You can also search for this author inPubMed Google Scholar * Valentina Tremaroli View author publications

You can also search for this author inPubMed Google Scholar * Björn Fagerberg View author publications You can also search for this author inPubMed Google Scholar * Dina Petranovic View

author publications You can also search for this author inPubMed Google Scholar * Fredrik Bäckhed View author publications You can also search for this author inPubMed Google Scholar * Jens

Nielsen View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.N., F.B., D.P. and B.F. conceived and designed the project. F.H.K., F.F., and

V.T., performed the experiments. F.H.K. and I.N. analysed the sequence data. All authors contributed writing and editing the manuscript. F.B. and J.N. contributed equally to the study.

ACCESSION CODES: Gut metagenome sequences have been deposited in the Sequence Read Archive under accession code SRA059451. CORRESPONDING AUTHORS Correspondence to Fredrik Bäckhed or Jens

Nielsen. ETHICS DECLARATIONS COMPETING INTERESTS J.N and F.B. are shareholders in MetaboGen AB. All other authors declare no competing financial interests. SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary Figures S1-S19 and Supplementary Tables S1-S3 (PDF 284 kb) SUPPLEMENTARY DATA 1 Reference genomes included in the database for taxonomic assignment of

metagenomic reads. (XLS 815 kb) SUPPLEMENTARY DATA 2 Core microbiota in our cohort. Genomes with a coverage of more than 1% in all 27 subjects. (XLS 36 kb) RIGHTS AND PERMISSIONS This work

is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 Unported License. To view a copy of this license, visit

http://creativecommons.org/licenses/by-nc-nd/3.0/ Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Karlsson, F., Fåk, F., Nookaew, I. _et al._ Symptomatic atherosclerosis is

associated with an altered gut metagenome. _Nat Commun_ 3, 1245 (2012). https://doi.org/10.1038/ncomms2266 Download citation * Received: 24 September 2012 * Accepted: 07 November 2012 *

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